Each year millions of Americans are diagnosed with diseases such as diabetes, heart disease, Alzheimer's, and various forms of cancer. In concert with environmental variation, risk for these diseases is controlled by large, heterogeneous sets of genetic factors. By characterizing those genes that increase risk, the biomedical community can describe the molecular pathways involved in human health and disease, ultimately enabling the rational design of novel treatments. Unfortunately, progress towards identifying the catalog of genetic risk alleles has been slow. Despite our ability to carry out massive population-based case-control genetic association studies in humans, only a tiny fraction of causative sites are known for any given complex disease. Over the last few years several groups, including ourselves, have been exploring the utility of advanced generation, multiparental mapping panels for routine, powerful, and high-resolution dissection of complex trait variation in model genetic systems (e.g., the mouse Collaborative Cross, the rat NIH heterogeneous stock, the Drosophila Synthetic Population Resource, or DSPR). Model systems exhibit similar genetic, cellular, physiological, and behavioral processes to humans, and offer a complementary avenue for obtaining insight into the factors that underlie complex trait variation. We successfully developed the DSPR during the first period of grant funding, and provide this as a freely-available community resource for the genetic dissection of trait variation in flies. Our project delivered a framework that can map causative variants to small genomic intervals, and has the power to map rare variants, and genes that harbor multiple causative alleles, both of which human association studies struggle to interrogate. Here we will continue to extend our work developing the DSPR as a powerful set of enabling resources for the Drosophila biomedical community. First, we will employ long-read, high-throughput sequencing to generate genome assemblies for all lines founding the DSPR. These novel assemblies will allow us to identify structural, and copy number variants in the lines, complementing our existing data to provide the complete catalog of segregating variation in the DSPR. Second, we will carry out genomewide gene expression profiling on multiple tissues for a large set of lines, allowing us to identify genes contributing to expression variation. Additionally, we will identify regions of the genome likely t recruit regulatory proteins. Integrating these datasets will facilitate the detection of causative, regulatory variants, which are thought to contribute significantly to trait variation. Finally, buiding on our considerable success mapping loci underlying biomedically-relevant trait variation in the DSPR, we will carry out extremely large-scale phenotyping screens for two traits in a testcross design (crossing each DSPR line to a set of unrelated strains). This work will allow us to detail the degree to which mapped loci have effects that are dependent on the genetic background of the mapping population. These results will have important implications for the design of mapping experiments in model systems.